Senior Data Scientist

As a Senior Data Scientist GenAI at our company, you will be responsible for leading the design, development, and deployment of advanced AI and Generative AI solutions. This customer-facing leadership role requires deep hands-on expertise across the entire AI lifecycle, from data engineering and model development to production deployment and team leadership. Key Responsibilities: - Lead end-to-end GenAI solution development, from data preprocessing to production deployment - Design and implement LLM, VLM, RAG, and Agentic AI systems - Build and optimize ML/DL models using modern and core techniques - Apply NLP Transformers and prompt engineering for enterprise use cases - Deploy scalable AI solutions using FastAPI/Flask with cloud infrastructure - Implement MLOps best practices, including CI/CD, Docker, and monitoring - Drive predictive analytics use cases, including time series and forecasting - Collaborate with customers and stakeholders to translate business needs into AI solutions - Lead, mentor, and manage high-performing data science teams Qualifications Required: - 10-15 years of experience in Data Science with a strong focus on GenAI - Hands-on expertise in Python, TensorFlow, PyTorch, and Keras - Strong experience with LLMs, RAG pipelines, and Agentic AI architectures - Solid understanding of ML, DL, NLP Transformers, and Computer Vision (CNNs) - Experience in production ML, MLOps, and cloud deployment (AWS / Azure) - Exposure to data engineering platforms such as Azure ADF and Data-bricks - Proven experience in customer-facing roles and team handling - Excellent communication skills; grounded, delivery-driven mindset In addition to the above, the following skills are required: - GenAI - LLMs - VLMs - RAG - Agentic AI - Python - Machine Learning - Deep Learning - NLP Transformers - Prompt Engineering - MLOps - Docker - CI/CD - FastAPI - Flask - AWS - Azure - Computer Vision - Time Series Forecasting - Databricks - Azure Data Factory As a Senior Data Scientist GenAI at our company, you will be responsible for leading the design, development, and deployment of advanced AI and Generative AI solutions. This customer-facing leadership role requires deep hands-on expertise across the entire AI lifecycle, from data engineering and model development to production deployment and team leadership. Key Responsibilities: - Lead end-to-end GenAI solution development, from data preprocessing to production deployment - Design and implement LLM, VLM, RAG, and Agentic AI systems - Build and optimize ML/DL models using modern and core techniques - Apply NLP Transformers and prompt engineering for enterprise use cases - Deploy scalable AI solutions using FastAPI/Flask with cloud infrastructure - Implement MLOps best practices, including CI/CD, Docker, and monitoring - Drive predictive analytics use cases, including time series and forecasting - Collaborate with customers and stakeholders to translate business needs into AI solutions - Lead, mentor, and manage high-performing data science teams Qualifications Required: - 10-15 years of experience in Data Science with a strong focus on GenAI - Hands-on expertise in Python, TensorFlow, PyTorch, and Keras - Strong experience with LLMs, RAG pipelines, and Agentic AI architectures - Solid understanding of ML, DL, NLP Transformers, and Computer Vision (CNNs) - Experience in production ML, MLOps, and cloud deployment (AWS / Azure) - Exposure to data engineering platforms such as Azure ADF and Data-bricks - Proven experience in customer-facing roles and team handling - Excellent communication skills; grounded, delivery-driven mindset In addition to the above, the following skills are required: - GenAI - LLMs - VLMs - RAG - Agentic AI - Python - Machine Learning - Deep Learning - NLP Transformers - Prompt Engineering - MLOps - Docker - CI/CD - FastAPI - Flask - AWS - Azure - Computer Vision - Time Series Forecasting - Databricks - Azure Data Factory